 pandas: powerful Python data analysis toolkit - 0.13.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 3 Frequently Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’ format. This default format can be set as an option by setting io.hdf.default_format. In [44]: path = ’test.h5’ In [45]: df = DataFrame(randn(10,2)) In [46]: df.to_hdf(path,’df_table’,format=’table’) 180 码力 | 1219 页 | 4.81 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.13.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 3 Frequently Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’ format. This default format can be set as an option by setting io.hdf.default_format. In [44]: path = ’test.h5’ In [45]: df = DataFrame(randn(10,2)) In [46]: df.to_hdf(path,’df_table’,format=’table’) 180 码力 | 1219 页 | 4.81 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.14.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 3 Frequently the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1349 页 | 7.67 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.14.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 3 Frequently the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1349 页 | 7.67 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.15the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1579 页 | 9.15 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.15the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1579 页 | 9.15 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.15.1the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1557 页 | 9.10 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.15.1the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern the rest of pandas (GH5129). • Bug in read_html tests where redirected invalid URLs would make one test fail (GH6445). • Bug in multi-axis indexing using .loc on non-unique indices (GH6504) • Bug that Format Changes. A much more string-like query format is now supported. See the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’0 码力 | 1557 页 | 9.10 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.17.0col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') In [34]: df = pd.read_excel('test.xlsx', header=[0,1], index_col=[0,1]) In [35]: df Out[35]: col1 foo bar col2 ValueError (GH10505) • Bug in groupby(axis=1) with filter() throws IndexError (GH11041) • Bug in test_categorical on big-endian builds (GH10425) • Bug in Series.shift and DataFrame.shift not supporting the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern0 码力 | 1787 页 | 10.76 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.17.0col1 foo bar col2 a b a b i1 i2 j l 1 2 3 4 k 5 6 7 8 In [33]: df.to_excel('test.xlsx') In [34]: df = pd.read_excel('test.xlsx', header=[0,1], index_col=[0,1]) In [35]: df Out[35]: col1 foo bar col2 ValueError (GH10505) • Bug in groupby(axis=1) with filter() throws IndexError (GH11041) • Bug in test_categorical on big-endian builds (GH10425) • Bug in Series.shift and DataFrame.shift not supporting the group name (GH7313). • Bug in isnull() when mode.use_inf_as_null == True where isnull wouldn’t test True when it encountered an inf/-inf (GH7315). • Bug in inferred_freq results in None for eastern0 码力 | 1787 页 | 10.76 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.25a development environment if you wish to create a pandas development environment. 2.3 Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- �→packages\pandas ============================= test session starts ============================= ==================== 12130 passed, 12 skipped in 368.339 seconds ===================== 2.3. Running the test suite 7 pandas: powerful Python data analysis toolkit, Release 0.25.3 2.4 Dependencies Package0 码力 | 698 页 | 4.91 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.25a development environment if you wish to create a pandas development environment. 2.3 Running the test suite pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- �→packages\pandas ============================= test session starts ============================= ==================== 12130 passed, 12 skipped in 368.339 seconds ===================== 2.3. Running the test suite 7 pandas: powerful Python data analysis toolkit, Release 0.25.3 2.4 Dependencies Package0 码力 | 698 页 | 4.91 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.3 Dependencies With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5 . . . . . 388 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 viii 3.5.5 Running the performance test suite . . . . . . . . . . . . . .0 码力 | 2045 页 | 9.18 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.20.3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 2.3 Dependencies With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5 . . . . . 388 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 viii 3.5.5 Running the performance test suite . . . . . . . . . . . . . .0 码力 | 2045 页 | 9.18 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.20.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.3 Dependencies With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5 . . . . . . . 386 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.5 Running the performance test suite . . . . . . . . . . . . . . .0 码力 | 1907 页 | 7.83 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.20.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.2.6 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 2.3 Dependencies With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 3.5.3 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5 . . . . . . . 386 3.5.4 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.5 Running the performance test suite . . . . . . . . . . . . . . .0 码力 | 1907 页 | 7.83 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.3 Dependencies standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 3.5.2 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Writing . . . . . . . . . . . 335 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running the performance test suite . . . . . . . . . . . . . . . . .0 码力 | 1937 页 | 12.03 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 2.3 Dependencies standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 3.5.2 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Writing . . . . . . . . . . . 335 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 Running the performance test suite . . . . . . . . . . . . . . . . .0 码力 | 1937 页 | 12.03 MB | 1 年前3
 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.3 Dependencies standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 3.5.2 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Writing . . . . . . . . . . . 337 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running the performance test suite . . . . . . . . . . . . . . . . .0 码力 | 1943 页 | 12.06 MB | 1 年前3 pandas: powerful Python data analysis toolkit - 0.19.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.2.7 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 2.3 Dependencies standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 3.5.2 Test-driven development/code writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Writing . . . . . . . . . . . 337 Running the test suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Running the performance test suite . . . . . . . . . . . . . . . . .0 码力 | 1943 页 | 12.06 MB | 1 年前3
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